用有限视野传感器数据，预测周围环境动态状态。
Predicting Dynamic Map States from Limited Field-of-View Sensor Data
- 将时序传感器数据转为时空图像，适配现有图像模型
- 在多种场景下实现高精度地图状态预测
- 适合自动驾驶、机器人等受限视野应用
当自主系统部署于真实场景时，传感器常受视场（FOV）限制，或因设计、遮挡或故障导致视野不足。在大视场不可用的情况下，需基于可用数据推断环境信息并预测周边状态，以保障安全准确运行。本文研究基于有限视场时序数据的深度学习动态地图状态预测方法。通过将动态传感器数据简化为同时包含空间与时间信息的单张图像表示，可有效利用大量现有的图像到图像学习模型，在多种感知场景中实现高精度的地图状态预测。
原文摘要 · Abstract (English)
When autonomous systems are deployed in real-world scenarios, sensors are often subject to limited field-of-view (FOV) constraints, either naturally through system design, or through unexpected occlusions or sensor failures. In conditions where a large FOV is unavailable, it is important to be able to infer information about the environment and predict the state of nearby surroundings based on available data to maintain safe and accurate operation. In this work, we explore the effectiveness of deep learning for dynamic map state prediction based on limited FOV time series data. We show that by representing dynamic sensor data in a simple single-image format that captures both spatial and temporal information, we can effectively use a wide variety of existing image-to-image learning models to predict map states with high accuracy in a diverse set of sensing scenarios.
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